An integrated cost-based approach for real estate appraisals

被引:16
|
作者
Guo, Jingjuan [1 ]
Xu, Shoubo [1 ]
Bi, Zhuming [2 ]
机构
[1] Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100044, Peoples R China
[2] Indiana Univ Purdue Univ, Dept Engn, Ft Wayne, IN 46805 USA
来源
INFORMATION TECHNOLOGY & MANAGEMENT | 2014年 / 15卷 / 02期
基金
中国国家自然科学基金;
关键词
Technological economics; Information management for real estate appraisals; Financial information systems; Financial information management; MATERIAL FLOW; NEURAL-NETWORK; SYSTEMS; PERSPECTIVE;
D O I
10.1007/s10799-012-0152-7
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
Real estate appraisal information systems have been studied by many researchers in the past including those systems that have integrated geographic information systems, artificial neural networks, etc. This paper proposes a new integrated approach for real estate appraisals which can be used in real estate appraisal systems to improve efficiency and accuracy. Motivated by the identified limitations of existing cost approaches for real estate appraisals, we integrate some elements from sales comparison approach and income approach into the cost approach to improve the accuracy of the valuation of real estate appropriately. As a result, the new integrated cost-based approach is capable of taking all of the major factors into accounts; these factors are closely related to the assets of real estate in one way or another. In the implementation of the new approach: (1) the concept of replacements cost is revisited and expanded to consider dynamic, environmental, and cultural factors in real estate appraisals; (2) the conventional depreciation values and depreciation rates are replaced by adjustment values and coefficients to include both the positive and negative impact on the changes of real estate value; (3) the theory of technology economics is applied, six forces have been systematically analyzed to determine replacement costs; and finally, (4) different methods for value adjustments, including the algorithm based on artificial neural network, have been utilized to deal with the randomness and uncertainties of mass data for the determination of adjustment values and coefficients.
引用
收藏
页码:131 / 139
页数:9
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